sample_age4grp_experiment.yaml

experiment_setup_parameters:
  age_bins:
    - age0to19
    - age20to39
    - age40to59
    - age60to100
fixed_parameters_region_specific:
  N:
    expand_by_age: True
    'NMH_catchment': [78401,96245,79829,60526]
    #'Chicago': ,
    #'IL': ,
    'EMS_1':  [170342, 184052, 203274, 182064]
    'EMS_2':  [246836, 291953, 311148, 279459]
    'EMS_3':  [128558, 147943, 181366, 163605]
    'EMS_4':  [156230, 178549, 199940, 166815]
    'EMS_5':  [89601, 103718, 113788, 112211]
    'EMS_6':  [170529, 215422, 212023, 194309]
    'EMS_7':  [459863, 455969, 532097, 377955]
    'EMS_8':  [406488, 439749, 492527, 344784]
    'EMS_9':  [482757, 506403, 591629, 399718]
    'EMS_10': [252497, 252957, 313926, 238921]
    'EMS_11': [656456, 889748, 697571, 500905]
fixed_parameters_global:
  initialAs:
    expand_by_age: True
    list: [3, 3, 3, 3]
  speciesS:
    expand_by_age: True
    custom_function: subtract
    function_kwargs: {'x1': N, 'x2': initialAs}
  C:  # all locations
    matrix:  # Unnormalized contact matrix 
    - [1.14254471871631, 0.126439684412323, 0.0353749597652072, 0.106736939258122] 
    - [0.126439684412323, 2.88407596706098, 0.12307674904548, 0.0970738335146058] 
    - [0.0353749597652072, 0.12307674904548, 0.167237414993413, 0.0332514267842826] 
    - [0.106736939258122, 0.0970738335146058, 0.0332514267842826, 0.0524833539522054]
sampled_parameters:
  'time_to_infectious':
    np.random: uniform
    function_kwargs: {'low': 1, 'high': 2}
  time_to_hospitalization:
    EMS_11:
      # Give a region-specific distribution by nesting it under the region name
      np.random: uniform
      function_kwargs: {'low':2, 'high':10}
    EMS_10:
      # If expanding by age, provide a list of kwargs
      # (and optionally a list of distribution names)
      # with the same lengths as the age_bins.
      # If only a single distribution name is given, it will be used
      # for all age bins.
      expand_by_age: True
      np.random: uniform
      function_kwargs:
        - {'low': 2, 'high': 4}
        - {'low': 3, 'high': 5}
        - {'low': 5, 'high': 7}
        - {'low': 7, 'high': 9}
    # Any distribution not nested under a region will be used for regions
    # which are not otherwise specified.
    np.random: uniform
    function_kwargs: {'low':15, 'high':30}
  recovery_time_crit:
    # If "expand_by_age" is True, but only one distribution is provided,
    # the same distribution will be used for each age group.
    expand_by_age: True
    np.random: exponential
    function_kwargs: {'scale': 0.5}
  recover_rate_crit_age0to19:
    # You can manually enter the distribution for an age group.
    # If it comes after an automatically generated set of age distributions,
    # as with the default `recovery_time_crit` above, it will overwrite the default.
    # NB: Order matters! The last distribution will be used for the age bin.
    np.random: exponential
    function_kwargs: {'scale': 1.5}
fitted_parameters: